The functionality of a neural network depends on its connectivity, hence, if we want to be able to investigate different properties of the network it is important to be able to accurately reconstruct the connectivity. Functional connectivity provides a strategy to construct the connectivity, which rather than focusing on the anatomical connectivity, takes an input-output driven approach. In this way, the connectivity is analyzed, directly from the neural activity, from the point of view of the statistical dependences among the data [1]}, [2]}. Crucial to the success of such an approach is how to model the relationship between the spike count data in order to build a reasonable estimate of these dependences.
